Continual crop development profiling using dynamical extended range weather forecasting with routine remotely-sensed validation imagery

ABSTRACT

A modeling framework for estimating crop growth and development over the course of an entire growing season generates a continuing profile of crop development from any point prior to and during a growing season until a crop maturity date is reached. The modeling framework applies extended range weather forecasts and remotely-sensed imagery to improve crop growth and development estimation, validation and projection. Output from the profile of crop development profile generates a combination of data for use in auxiliary farm management applications.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This patent application claims priority to, and is a continuation of,U.S. non-provisional application Ser. No. 14/853,593, filed on Sep. 14,2015, the contents of which are incorporated in their entirety herein.In accordance with 37 C.F.R. § 1.76, a claim of priority is included inan Application Data Sheet filed concurrently herewith.

FIELD OF THE INVENTION

The present invention relates to crop development profiling in precisionagriculture. Specifically, the present invention relates to a system andmethod of augmenting growing degree day models with dynamical extendedrange weather forecasts and routine remotely-sensed validation imageryfor developing profiles of crop growth throughout a crop growing season.

BACKGROUND OF THE INVENTION

The relationship between weather and plant growth has been recognizedfor many centuries. For example, the importance of inter-annualvariations of weather conditions for resulting differences in plantdevelopment levels is well-known. The understanding of the relationshipof weather to plant and animal growth, known as phenology, has become animportant process in annual estimation of crop stages, pest emergence,and disease development. Modern phenology began with recordings of plantand insect development in relation to climate conditions, and over thepast fifty years, the development of phenological models has acceleratedto provide crop-specific models, and even crop variety-specific models.The models, which are commonly referred as growing degree day (GDD)models, have been developed to understand the timing between accumulatedgrowing degrees and stages of crop development. Combined with pest anddisease emergence, which are also phenological processes, the growingdegree day models permit producers and crop advisors to make criticaldecisions on crop protection and enhancement.

Growing degree day models, whether specifically for issues such asinsects or diseases planning, or more generally for plant growth, werederived with the understanding that sensible heat derived fromatmospheric temperatures drives metabolic processes, affecting the rateof growth and development. In simple terms, a growing degree day is anindex of the amount of heat accumulated in a day to drive the metabolicprocess related to growth or development. Research in the pasthalf-century has led to the establishment of mathematical equations thatpredict the rate of development at different temperatures for a largeselection of crops, and for crop varieties. These mathematical equationsare used to predict the rate of insect, disease, or plant development astemperatures fluctuate over time, and provide guidance on upcomingstages of growth and measures to respond to impacts of such growth.

Throughout the duration of a growing season, the speculation of finalcrop development maturity and the rate of crop development is animportant consideration for a grower. From the time of planting andcontinuing through until the end of a growing season, the uncertainty ofhow a crop will mature, along with when and with what yield, areconsiderations of farmers when estimating final cash flows anddetermining the timing of inputs and actions to support crop developmentand farm business profitability. Traditional use of crop growth models,such as accumulated growing degree models, utilize accumulated weatherconditions that have occurred from crop planting to the current date toprovide a historical profile of development. Short-term weatherforecasts are also used, but this provides only limited additionalinsight into crop development for near-term crop management decisionsand does not provide a longer-range view of risks and expectations.

Therefore presently-used and known methods in modeling crop growth anddevelopment do not provide an adequate estimation of how a crop willdevelop over a longer range, such as through a remainder of the growingseason. The application of climatological data for the crop'sgeographical region fails to provide anything more than a looseapproximation as to development for the remainder of the growing season,since climatological data does not reliably represent a given year'sdaily conditions.

BRIEF SUMMARY OF THE INVENTION

It is one objective of the present invention a system and method ofestimating crop growth and development over an entire growing seasonusing long-range climatological and/or meteorological prediction andforecasting. It is another objective of the present invention to apply aparadigm of long-range weather prediction that is responsive to globalweather anomalies, and tuned for anticipated regional weather trendsduring the entire growing season, to provide a realistic estimation of acrop growth profile from planting date to full crop maturity.

It is another objective of the present invention to augment growingdegree day modeling for crop growth development and profiling withlong-term weather forecast and prediction, together with routineremotely-sensed crop validation imagery. It is yet another objective ofthe present invention to provide a system and method of crop developmentprofiling which generates output data that serves as a valuabledecision-support tool for projecting future activities in cropmanagement.

The present invention is a system and method of estimating crop growthand development over the course of an entire growing season. The presentinvention utilizes dynamical extended range weather forecasts spanningthe entire duration of the remaining growing season to improve cropgrowth and development estimation. Using crop growth models, basedprimarily upon growing degree-days, the present invention generates aprofile of crop development until the end of a growing season. Thepresent invention contemplates that in one embodiment, these crop growthmodels may be executed on a periodic or frequent basis, such as a weeklybasis, using an update to the dynamical extended range weather forecaststhat consider the ongoing evolution of the region's seasonal weatherconditions. Such a generation of a new crop growth and developmentprofile each week may include the growth model output to date using theanalyzed and/or observed weather conditions, along with the forecastedweather through the remainder of the growing season. Output from thecrop growth and development profile generates a combination of data foruse in auxiliary farm management applications. This output may bepresented in many ways, such as for example as a graphical display ofthe crop development attributes as it correlates to observed andpredicted weather.

The present invention also utilizes remotely-sensed measurements, suchas imagery of crop vitality as depicted from plant spectral analyses,alongside the dynamical extended range weather forecasts. This may beaccomplished using techniques such as for example normalized differencevegetation indices (NDVI), which are generated periodically for cropfields supported by growth modeling. Combining spectral analyses of cropvitality with the weather-driven crop growth model results provide avisual reference to the spatial variability across the field of the cropgrowth model output. As such, remotely-sensed imagery permits extensionof crop growth model results beyond the single point represented by themodel's execution. This provides both a validation of the crop growthmodel and serves as a benchmark for evaluation of the forward-lookingestimation of crop growth and development using the weekly weatherforecast updates of the crop growth model.

Other objects, embodiments, features and advantages of the presentinvention will become apparent from the following description of theembodiments, taken together with the accompanying drawings, whichillustrate, by way of example, the principles of the invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate several embodiments of theinvention and together with the description, serve to explain theprinciples of the invention.

FIG. 1 is a block diagram of data flow within components of an augmentedcrop growth model according to the present invention; and

FIG. 2 is systemic diagram of an augmented crop growth model accordingto the present invention.

DETAILED DESCRIPTION OF THE INVENTION

In the following description of the present invention reference is madeto the exemplary embodiments illustrating the principles of the presentinvention and how it is practiced. Other embodiments will be utilized topractice the present invention and structural and functional changeswill be made thereto without departing from the scope of the presentinvention.

FIG. 1 is a block diagram of information flow within an augmented cropgrowth model 100 for projecting crop development throughout an entiregrowing season. The augmented crop growth model 100 is performed withinone or more systems and/or methods that includes several components,including some that are replicated for historical/current and predictiontime periods. Each of these components define distinct activitiesrequired to support projection of crop maturity from prior to plantingat the beginning of a growing season, through to crop maturation andharvesting at the completion of a growing season.

The execution of the augmented crop growth model 100 prior to plantingat the beginning of a growing season addresses multiple factors inplanning production activities for the year. It addresses a seriouschallenge, for example, of knowing whether there will be sufficientlength to a growing season for a crop to reach its full maturity.Simulating a full growing season before planting provides a projectionof the expected timeline of crop growth stages and anticipated date forcrop maturity and harvest. This advanced planning increases thelikelihood of success during the growing season.

Such advanced planning also has a number of other advantageous uses tothe producer or grower. For example, the anticipated date for cropmaturity and harvest can be used to determine whether a crop willaccumulate sufficient growing degree-days for crop completion. Wherethis is not expected the producer may make changes to crop selection,change to a shorter season variety of the original crop type, or alterthe date of crop planting to permit adequate time for crop maturity tooccur. A timeline of crop growth provided prior to planting also givesinsight on critical dates for potential pests and disease supportingplanning decisions for herbicide and pesticide requirements during theupcoming growing season. This also provides support for anticipatingwhen fertilizer applications are needed or addition of nutrients tofinish the crop development.

The augmented crop growth model 100 ingests input data 110 that includescrop specifications 111 such as for example the type and variety ofcrop, and other planting data 112, such as for example the date that afield was planted with seed. The input data 110 also includesremotely-sensed imagery data 113, such as for example those capturedfrom orbiting satellites that when processed provide details at a fieldlevel resolution. The input data 110 further includes meteorologicaldata 114, which includes both dynamical extended range weather forecastinformation 115 through to the completion of a growing season, as wellas observed weather data 116 that has occurred during the currentgrowing season and current field-level weather data 117. All of thisinput data is ingested into the augmented crop growth model 100 via adata ingest module 210 as shown in the system diagram of FIG. 2.

The augmented crop growth model 100 ingests these inputs to generate acrop development profile 250, such as for example in a growing degreeday model, by describing the heat accumulated to support growth anddevelopment. This is used to generate instances of an accumulatedgrowing degree day estimate 120 in an estimation module 220 for timeperiods throughout a growing season, such as daily or weekly. Theaugmented crop growth model 100 also ingests field-level remotely-sensedimage data 113, and performs a crop growth validation 140 using suchimagery in a validation module 230. The augmented crop growth model mayalso ingest, as noted above, meteorological information 114 in the formof dynamical extended range weather forecast data 115, which is used toperform additional instances of generating an accumulated growing degreeday estimates 120 in the estimation module 220 to continue building thecrop development profile 250. The augmented crop growth model 100 thenperforms a projection of accumulated growing degree days to cropmaturity 130 in a projection module 240, which contributes to aprogression of the crop development profile 240 to the end of thegrowing season.

The crop development profile 250 is therefore output data 150 of theaugmented crop growth model 100. Once the augmented crop growth model100 has completed this projection phase 140, the present inventiondetermines whether a crop has reached maturity 151. This determines aform of one type of output data 150. Where a crop has reached maturityat step 151, the model 100 generates crop summary reports 152. If cropmaturity has not been reached at step 151 (and therefore the growingseason is still underway), the model 100 generates crop growth andaction reports 154. Output data 150 may further be used for generationof advisory services 270 in one or more application programminginterface (API) modules 260 as described in more detail herein.

System components of the augmented crop growth model 100 include one ormore data processing modules 282 within a computing environment 280 thatalso includes one or more processors 284 and a plurality of software andhardware components. The one or more processors 284 and plurality ofsoftware and hardware components are configured to execute programinstructions to perform the functions of the augmented crop growth model100 described herein, and embodied in the one or more data processingmodules 282.

The crop specifications 111 and the planting data 112 provide variousaspects of the crop information that form a basis for the configurationof the phenological processes analyzed in the augmented crop growthmodel 100 of the present invention. The crop specifications 111 providetwo general categories of information, which are the type of crop to beplanted, and the variety, or cultivar, of that crop type. For example, acrop type could be corn and the variety of corn could be standard,sugary enhanced, or super-sweet. The type of crop determines a set ofgenerally defined growth characteristics of the crop, such as occurrenceof various leave stages and produce development stages for the croptype. Specifying the variety fine-tunes these growth characteristics tospecify the timing to each stage, nutrient requirements per stage,pest/disease susceptibility per stage, and the temperature thresholdsfor grow/no growth of the crop. These variety characteristics areestablished as a particular crop variety is genetically developed byresearchers and are often associated with a particular seed productname. Crop specifications 111 may further include crop practices, suchas whether the crop is organic, genetically modified, non-geneticallymodified, irrigated, and non-irrigated. Together, the type of crop andits variety informs the generation of the crop development profile 250for the crop and establishes a basis for analyzing related factors suchas pests and diseases. This information on pests and disease providesinformation on required weather conditions that lead to the emergence ofpests and disease and includes information on eradication measuresshould they occur. The presence of factors such as these is important atparticular times during specific growth stages and throughout the growthcycle, and becomes a significant part of notification processes withinone or more advisory services 270 within the present invention.

Other crop specifications 111, such as the precise location of cropfield, provide a basis for determining weather conditions during thegrowing season. These locations can be expressed either in positionalcoordinates such as latitude and longitude, and may also be expressed interms of platting data representing boundaries and location within atownship, area, range, or section. Regardless, the present invention mayincorporate appropriate algorithms to extract data such as thepositional coordinates and platting data.

Planting information 112 such as the planting date may be provided aseither an anticipated date or an actual date. The anticipated plantingdate may be used, for example, where activity is being modeled in theaugmented crop growth model 100 prior to the growing season. Otherwise,the actual date of planting is used. It should be noted in the case ofmodeling for pre-growing season activity that the augmented crop growthmodel 100 may be reset when the crop is planted and the actual plantingdate becomes known. Other planting information 112 includes soilinformation such as the soil type, current and/or forecasted soilconditions, overall soil profile, levels of vegetative debris existingat the time of planting, nutrient levels present at planting, and thesoil moisture history since the previous crop. Still other plantinginformation 112 may include plant depth, plant population, and rowwidth. Planting information becomes a significant part of notificationprocesses within one or more advisory services 270 within the presentinvention

Meteorological data 114 is collected for the specified geographical areawhich a crop is located, using either temperature sensors in the cropfield or from fine-resolution analyses of weather derived from sensorsacross a region that includes the crop field, or both (since thepresence of temperature sensors within crop fields for all possibleinstantiations of the augmented crop growth model 100 may not befeasible). For the purpose of the phenological processes modeled in thepresent invention, these temperature sensors may be configured tocontinuously monitor maximum and minimum temperatures over 24-hourperiods. The spatial analysis of observed, sensible air temperatures mayemploy a Barnes objective analytical scheme that permits exponentialweighting functions to maximize the information of closer observationsto the location of interest. This interpolation is a multi-pass methodgiven by the equation:

$\begin{matrix}{{g\left( {x_{i},y_{j}} \right)} = {{g_{0}\left( {x_{i},y_{j}} \right)} + {\sum{\left( {{f\left( {x,y} \right)} - {g_{0}\left( {x,y} \right)}} \right){\exp\left( {{- \gamma}\;\kappa\frac{\pi^{2}}{\lambda}} \right)}}}}} & (1)\end{matrix}$where g₀ (x_(i),y_(j)) is the initial estimated value at a grid locationas given by the equation:

$\begin{matrix}{{g_{0}\left( {x_{i},y_{j}} \right)} = {\frac{\sum\limits_{k}{w_{ij}{f_{k}\left( {x,y} \right)}}}{\sum\limits_{k}w_{ij}}.}} & (2)\end{matrix}$

The value of γ controls the smoothing of data, while λ is an expressionof the minimum wavelength of disturbances resolved in the analysis. Theweighting function, w, is specified at each m data points as anexponentially decreasing function of the distance, r, away from the gridpoint by the equation:

$\begin{matrix}{w_{ij} = {\exp\left( {- \frac{r_{m}^{2}}{\kappa}} \right)}} & (3)\end{matrix}$where κ controls the width of the Gaussian function. However, once themaximum and minimum temperatures are obtained either from in-situsensors or from spatially analyzed observations, data are stored in theapplication database for routine use by augmented crop growth model 100.

The canonical form of the growing degree day calculation is expressedas:

$\begin{matrix}{{G\; D\; D} = {\left\lbrack \frac{\left( {T_{Max} + T_{Min}} \right)}{2} \right\rbrack - T_{Base}}} & (4)\end{matrix}$where T_(Max) is the daily maximum sensible air temperature, T_(Min) isthe daily minimum sensible air temperature, and T_(Base) is the sensibleair temperature where growth process do not occur. The value forT_(Base) varies by crop type and even by cultivars of a crop type.Measurement or analysis of daily maximum and minimum sensible airtemperatures is used to describe the heat accumulated to support growthand development. As such the value of a growing degree day is to refernot to actual days but rather a representation of heat accumulation fora day that can be used to estimate the phyllochron of plant growth andeventual plant maturity.

Using a climatological average temperature for a given location, theaccumulated growing degree day at that location can be used to expressthe thirty-year average for the number of days elapsed since planting.Most crops exhibit a physiological growth limit when exposed to largeamounts of heat. Thus, Eq. 1 above is often modified to place limits ongrowing degree day accumulation during periods where temperatures exceedan upper threshold value. The same is true where either T_(Max) orT_(Min) fall below the base temperature threshold for plant growth. Thepresent invention may use the diurnal maximum and minimum single valuesof sensible air temperature gathered either by direct fieldobservations, by spatial analysis of surrounding observed sensible airtemperatures, or through prediction of daily maximum and minimumsensible air temperature from numerical weather prediction modelsdescribed below. Other methods of determining daily average sensible airtemperatures may also be utilized, such as summing all hourly values toderive a twenty-four hour average.

Regardless of the method used, multiple instances of the accumulatedgrowing degree day estimation are contemplated by the present invention.Initial and ongoing instances involve the use of historical and currentsensible air temperatures gathered and/or analyzed since crop planting.This may be represented by the observed weather data 116 that hasoccurred during the current growing season and the currently-experiencedfield-level weather data 117. The resulting accumulated growing degreeday estimate 120 represents the phenological activity of the cropdesignated during the initial and ongoing phases of the augmented cropgrowth model 100. An initial growing degree day calculation for the cropserves as the first marker on the crop development profile 250 along thepath to the crop maturity date. Each subsequent calendar day (or othertime interval as specified by a user of the present invention) providesan additional marker on the crop development profile 250 and serves as areference point for assessing factors such as potential insect anddisease hazards in the short-term growth the crop.

The use of field-level processed remotely-sensed imagery 113 providesadditional support for an assessment of current crop state and growthstage in the crop-growth validation phase 130 of the augmented cropgrowth model 100. One source of image data representing thisremotely-sensed imagery 113 is satellite systems, such as fine temporalresolution low-earth orbit satellites that provide a minimum of threespectral bands. Other sources are also contemplated, such as for exampleunmanned aerial systems, manned aerial reconnaissance, lower temporalfrequency earth resources satellite such as LANDSAT and MODIS,ground-based robots, and sensors mounted on field and farm equipment.Regardless of the source, this imagery 113 is field-navigated to provideusers with the most recent high-resolution depiction of the crop field.Imagery 113 in the form of image data may be delivered on a web orapplication-based tool configured within the augmented crop growth model100, and additional tools may be provided for spatially navigating theimage data and overlaying a variety of weather data elements.

The field-level remotely-sensed imagery 113 is used to map the cropfield and generate a time-series profile 250 of crop development andvitality. Both direct provision of field-level processed remotely-sensedimagery 113 and imagery 113 that has been analyzed may be provided.Imagery 113 is analyzed using a normalized difference vegetative index(NDVI) that provides the user with an evaluation of plant health,biomass and nutrient content. Other approaches may also be employed forsuch analysis, such as: Modified Chlorophyll Absorption RatioIndex/Optimized Soil-Adjusted Vegetation Index (OSAVI), TriangularChlorophyll Index/OSAVI, Moderate Resolution Imaging SpectrometerTerrestrial Chlorophyll Index/Improved Soil-Adjusted Vegetation Index(MSAVI), and Red-Edge Model/MSAVI. Regardless, analysis of imagery 113provides a meaningful further input to the augmented crop growth model100 because plants absorb short-wave radiation from the sun betweenwavelengths of 400-nm and 750-nm, which is their photo-syntheticallyactive spectral region. Thus, healthy plants appear darker at thesewavelengths. The addition of leaves on healthy plants results in strongreflection in the near infrared spectrum. These features provide thebasis for categorizing plant species as well as monitoring vitality ofplants using the NDVI analysis. The NDVI relationship is given for thered and green spectral bands in this application as

$\begin{matrix}{{N\; D\; V\; I_{red}} = \frac{\left\lbrack {\frac{{NIR}_{ref}}{{NIR}_{inc}} - \frac{{Red}_{ref}}{{Red}_{inc}}} \right\rbrack}{\left\lbrack {\frac{{NIR}_{ref}}{{NIR}_{inc}} + \frac{{Red}_{ref}}{{Red}_{inc}}} \right\rbrack}} & \left( {5a} \right) \\{{N\; D\; V\; I_{green}} = \frac{\left\lbrack {\frac{{NIR}_{ref}}{{NIR}_{inc}} - \frac{{Green}_{ref}}{{Green}_{inc}}} \right\rbrack}{\left\lbrack {\frac{{NIR}_{ref}}{{NIR}_{inc}} + \frac{{Green}_{ref}}{{Green}_{inc}}} \right\rbrack}} & \left( {5b} \right)\end{matrix}$

where NIR_(ref), Red_(ref), and Green_(ref) denote magnitudes ofreflected light in the near infrared, red and green bands and NIR_(inc),Red_(inc), and Green_(inc) denote magnitudes of incident in the samethree bands as the reflected.

Routine ingest of the remotely-sensed imagery 113 for the crop fieldprovides the time-series data needed to denote change in the crop, andis correlated with the crop growth stage estimated by the amount ofaccumulated growing degree days. This enables a comparison that allowsgrowers to calibrate the accumulated growing degree day estimates 120 toensure that appropriate decisions are made in relation to the cropgrowth actions suggested by the output data 150, particularly in thecrop summary report 154 discussed further herein.

In one embodiment of the present invention, the validation module 230may be configured to generate notifications to users of the augmentedcrop growth model 100 when a validation 130 identifies a change in acrop's growth. Such notifications ensure that appropriate decisions aremade in relation to the crop growth actions, and they may be provided tousers at times and to devices at the direction of the user. For example,when the validation phase 130 of the augmented crop growth model 100generates a change in crop growth, a tactile notification may beprovided to the user's mobile device, such as in a haptic vibration.Mobile devices may include any device capable of being used to view ormanipulate data generated by the present invention, whether it be adesktop, laptop, tablet, or notebook computer, or another mobile devicesuch as data-enabled telephone.

Further instances of the accumulated growing degree day estimation 120occur sequentially with, and following, prior/initial instances toperform the projection 140 of accumulated growing degree days to cropmaturity. These latter instances apply a dynamical extended rangeweather forecast 115 to provide daily maximum and minimum temperaturesfor the crop location for a period extending to the crop maturity dateprojected by the accumulated growing degree day estimation 120. In amanner similar to the daily accumulation of field-level sensible airtemperatures, gridded dynamical extended range weather forecasts 115 ofsensible air temperature are used to determine the predicted valuesusing the crop field's latitude and longitude. Daily projection 140 ofaccumulated growing degree day estimation 120 are made for eachsubsequent date beyond the current crop date and until the totalaccumulated growing degree day estimation 120 exceeds the crop maturityvalue specified for the crop at issue. Each day serves as a marker onthe crop development profile 250 in a manner similar to the accumulatedgrowing degree days using observed/analyzed sensible air temperaturesfrom the first instance of the crop growth model run. Thus, the combinedinstances of the accumulated growing degree day estimations 120 providean entire season of daily growing degree days for use in crop growthinterpretation.

As noted above, the use of phenological crop growth models, such asgrowing degree day models, to provide future expectations on growth havepreviously been limited by the scope of predicted weather informationincluded for estimating future growing degree days. Use ofclimatological normal values assumes that the current year will beequivalent to the average of the previous climate period that spannedthirty years. Unfortunately, the likelihood of matching this thirty-yearaverage condition, particularly summed over an entire growing season, isvery low and unrealistic. The present invention modifies thisclimatological average to account for annual and seasonal variations toenable a more realistic profile for projection of sensible airtemperatures (and also precipitation) for an entire growing season.

The augmented crop growth model 100 applies known methods forinter-seasonal to inter-annual climate prediction, which have evolvedinto a combination of deterministic and statistical modeling schemesthat permit the capture of long-term low-frequency features. Suchmodeling schemes are known as dynamical extend range weather forecastmodels. Dynamical extended range weather forecasting is one of the mostcomplex and challenging forecasting problems as it requires globalanalyses for the specification of observed initial and boundaryconditions, the use of sophisticated numerical weather predictionmodels, a statistical treatment of the model results, and a verificationof the forecast results as a feedback into forecast refinement.

The use of dynamical extend range weather forecasting in the augmentedcrop growth model 100 of the present invention involves accounting forthe future low frequency variability within the atmosphere to modify theclimatological expectations during the span of the growing season. Inone embodiment of the present invention, the modeling process involvesthe use of two data assimilation systems and two forecasting systems.The two data assimilation systems are used to provide historic andcurrent atmospheric and land surface initial conditions and also globalocean temperatures. The two forecast systems incorporate the U.S.National Centers for Environmental Predictions (NCEP) Global ForecastSystem (GFS) for atmospheric predictions and the Geophysical FluidDynamics Laboratory Modular Ocean Model to provide sea-surfacetemperature predictions. To provide computational efficiencies the GFSis run at a horizontal grid resolution of approximately 100-kilometersusing a spectral triangular truncation of 126 waves. To maintainconsistency between the data assimilation and atmospheric forecastingsystems, the same horizontal grid resolution is used for each. Softwareand data supporting the above are publicly available from the NCEP.

In the present invention, the dynamical extend range weather forecastmodels are executed daily to provide a complete global dataset for usein initializing the subsequent model run, and to be used to supplylocalized values for the growing degree day projections for individualcrop locations. Given the 100-kilometer grid spacing of the dynamicalextend range forecast (DERF) models, the data is downscaled to mappredicted values to each crop field location. This downscaling isaccomplished with a linear regression using the predicted values alongwith both observed conditions and error-corrected previous forecasts forthe desired location.

It should be noted that in the present invention, additional sources ofmeteorological data 114 may be utilized to provide one or more of theobserved weather data 116 that has occurred during the current growingseason, the current field-level weather data 117, and the dynamicalextended range weather forecasts 115, for example as data that iscomplementary to the two data assimilation systems and two forecastingsystems noted above. Such additional sources of weather data may includedata from both in-situ and remotely-sensed observation platforms. Forexample, numerical weather models (NWP) and/or surface networks may becombined with data from weather radars and satellites to reconstruct thecurrent weather conditions on any particular area to be analyzed. Thereare numerous industry NWP models available, and any such models may beused as sources of meteorological data 114 in the present invention.Examples of NWP models at least include RUC (Rapid Update Cycle), WRF(Weather Research and Forecasting Model), GFS (Global Forecast System)(as noted above), and GEM (Global Environmental Model). Meteorologicaldata 114 is received in real-time, and may come from several differentNWP sources, such as from Meteorological Services of Canada's (MSC)Canadian Meteorological Centre (CMC), as well as the National Oceanicand Atmospheric Administration's (NOAA) Environmental Modeling Center(EMC), and many others. Additionally, internally or privately-generated“mesoscale” NWP models developed from data collected from real-timefeeds to global observation resources may also be utilized. Suchmesoscale numerical weather prediction models may be specialized inforecasting weather with more local detail than the models operated atgovernment centers, and therefore contain smaller-scale data collectionsthan other NWP models used. These mesoscale models are very useful incharacterizing how weather conditions may vary over small distances andover small increments of time. The present invention may be configuredto ingest data from all types of NWP models, regardless of whetherpublicly, privately, or internally provided or developed.

Other sources of data ingested into the augmented crop growth model 100may include image-based data from systems such as video cameras, anddata generated from one or more vehicle-based sensing systems, includingthose systems coupled to computing systems configured on farm equipment,or those systems configured to gather weather data from mobile devicespresent within vehicles, such as the mobile telephony devices and tabletcomputers as noted above. Crowd-sourced observational data may also beprovided from farmers using mobile telephony devices or tablet computersusing software tools such as mobile applications, and from other sourcessuch as social media feeds. Meteorologist input may be still a furthersource of data.

The integration of crop growth status with projected sensible airtemperatures for the remainder of the growing season provide the groweror crop consultant a valuable decision-support tool for projectingfuture activities in crop management. Before the commencement of thegrowing season such estimations for the growing season identifyvariations in the thirty-year average climate for the upcoming growingseason that can lead to refined determination of crop selection, seedvariety and planting decisions. After planting, this informationprovides guidance on fertilizer management, irrigation, and applicationof herbicide and pesticide during the growing season.

Periodic crop growth action reports, provided at user-defined intervalssuch as daily, weekly, or bi-weekly, present the user with three primarycategories of information. First, it provides an updated developmentprofile of accumulated growing degree days since planting and identifiesthe transition of plant stages that have occurred and that are presentlyoccurring. Next, it provides a validation of the plant growth stagesusing remotely sensed imagery that includes a full NDVI analysis of thecrop field. Combined, these two parts provide a short-term roadmap onactions needed in crop management. The third part provides a detailed,daily accounting of future accumulated growing degree day up to cropmaturity. This latter includes future physiological stages and theirtiming. It also provides a summary of remaining threats to the cropincluding pests, potential diseases, and threats of drought andexcessive moisture. The time to maturity includes a specification of acalendar date when crop maturity is expected, and for harvest timing.

At the end of the crop growing season and with the last of the routinelygenerated crop growth action reports, a crop summary report isgenerated. This summary report provides an accumulation of crop growthprediction statistics along with a validation summary. This summaryreport provides growers with a snapshot of the growing season indicatinghow the growing season progress compared to the long-term averageexpectations for a growing season of the specific crop planted at thatlocation.

The present invention also contemplates that output data 150 may begenerated for visual representation of the information contained, forexample on a graphical user interface. For example, users may be able toconfigure settings for, and view various aspects of, the augmented cropgrowth model 100 using a display on such a graphical user interfaces,and/or via web-based or application-based modules. Tools and pull-downmenus on such a display (or in web-based or application-based modules)may also be provided to customize the estimation 120, validation 130,and projection 140 phases of the model 100, as well as the output data150. Examples of this include the tactile notifications of changes incrop growth discussed above. Other types of notifications may includethose provided via applications resident on mobile telephony, tablet, orwearable devices are also possible, such as for example a voice-basedoutput generated to verbally notify farmers of possible disease or pestrisk.

As noted above, the output data 150 of the augmented crop growth model100 may be used to generate a plurality of advisory services 270 in oneor more application programming interface (API) modules 260. Theseadvisory services 270 provide enhanced decision-making support toprecision agricultural production within the present invention.

These advisory services 270 are driven by the generation of the cropdevelopment profile 250 that results in the crop growth and actionreporting and crop summary reporting aspects of the output data 150.Each of these advisory services 270 enriches the utilization andapplication of the augmented crop growth model 100.

One such advisory service 270 is a current and historical weatherreporting service 271. Site-specific weather information is an importantelement of field recordkeeping in agricultural production. Using thefield position information provided for the accumulated growing degreeday calculations, current and historical weather for the field locationmay be provided as an advisory service for specific geographicallocations. Accordingly, this information has a dual utility in thepresent invention, as it is also utilized as a routine input data(observed weather data 116 and current field-level weather data 117) tothe accumulated growing degree day estimation 120 to provide the neededmaximum and minimum temperatures for computing actual growing degreedays. The present invention therefore contemplates using thisinformation as both as input to the augmented crop growth model 100 and,together with geo-positional information, as a form of output data 150as an advisory service 270.

Another advisory service 270 is an hourly and daily climatologyreporting service 272. Although the output data 150 is generally relatedto dynamical computation of growing degree days for a specific field andcrop type, users of the present invention are capable of contrasting thedynamical growing degree days with expected growing degree days relativeto the thirty-year climatology. This enables one to contrast thedifference experienced in a given year relative to the long-term averagederived from climatology data provided on an hourly or daily basis.Accordingly, hour-by-hour and daily climatological averages forspecified location(s) and time period(s) can be provided as a reportingservice 272. In addition, this reporting service 272 provides aprobability distribution of various weather-related metrics includingthe number of remaining growing degrees based upon climatology.

Another advisory service 270 is an hourly and daily weather forecastingservice 273. The use of routine weather forecast information is a stapleof agricultural production support operations and planning, and thisadvisory service 270 provides such weather forecast information toaugment the routine generation and reporting of accumulated growingdegree days. Accordingly, the present invention can be configured togenerate a weather forecast in conjunction with the accumulated growingdegrees day estimation 120 in the output data 150. The weather forecastservice 273 is available in multiple data formats ranging from text,site-specific graphics or as a map-based display on a graphical userinterface.

A soil modeling service 274 is another advisory service 270 contemplatedwithin the scope of the present invention. One API module 260 appliesthe crop development profile 250 and output data 150 to a sophisticatedsoil model to generate information that provides a better understandingof current and future soil conditions relative to historical soilconditions. Such a soil model support the use of existing soilproperties e.g., organic matter, soil type, etc., tillage practices,presence of tile drainage, and irrigation history, along with theadvanced long-range weather forecasting. This coupling of modeled soilcharacteristics with advanced weather forecasting information from thedynamical extended range forecast 115 for an entire growing seasonprovides a valuable tool for indicating crop potential. The output of anAPI module generating this soil modeling service 274 may be furtherconfigured to provide a detailed past-through-future analysis of soiltemperature and moisture assessments, including freeze and thawinformation, the amount of water ‘throughput’, runoff or ponding ofsoils, and an important interpretation of workability of the soils. Soilmodels contemplated for use in the present invention may include modelssuch as the EPIC, APEX, and ICBM soil models.

A planting prediction reporting service 275 may also be provided as anadvisory service 270. When utilized prior to the growing season usingthe season-long dynamic extended range weather forecasting capabilities,the augmented crop growth model 100 of the present invention can beconfigured to simulate an entire growing season before it occurs.Applying this to assess the timeline of accumulated growing degree daysthroughout the growing season when planted at differing times is aninherent benefit to optimizing successful completion of a selected cropand/or making an informed decision as to what type of crop or itscultivar to select. When coupled with an entire growing season's growingdegreed days estimation 120 and the weather output from the dynamicalextended range weather forecast 115, a planting prediction reportingservice 275 provides multi-faceted information regarding plantingoperations, such as near-term field conditions (soil moisture andtemperature) and expected seasonal growth metrics. This plantingprediction reporting service 275 provides likely periods of suitabilityof weather and soil conditions, including soil workability, soiltemperatures, potential adverse post-planting weather conditions, andthe likelihood that the crop will reach maturity. The plantingprediction reporting service 275 may also utilize the output data 150 inthe hourly and daily climatology reporting service 271 to provide afurther comparison of the dynamical extended range weatherforecast-driven growing degree days planting predictions to one basedupon climatology.

Another advisory service 270 is soil conditions forecasting service 276.The soil modeling service 274 can be used with predicted weather toprovide an estimate of future soil conditions in this advisory service270. Using the dynamical extended range forecast data 115, a forecast ofsoil temperature and moisture conditions for the specified fieldlocation can generated. Such a forecast in this service 275 enablesanticipation of periods of suitable soil conditions for fieldoperations, including workability, particularly those after periods ofrainfall.

Yet another advisory service 270 as an output of the augmented cropgrowth model 100 is a fertilizer forecasting service 277. Knowledge ofexpected growing degree days provides a timeline for the various stagesof crop growth, and each of these stages has differing nutrient needs.The fertilizer forecasting service 277 uses timing of these stagespredicted by the accumulated growing degree day timeline along with thesoil modeling data from the soil modeling service 274 to identifyperiods of suitability of soil conditions for continued satisfactorycrop growth, and anticipates the impact of soil saturation on the lossof available nutrients via leaching, denitrification, and other soil andwater related crop growth factors. The fertilizer forecasting service277 may also provide a recommendation of how much fertilizer should beapplied and an estimate of how much of the fertilizer applied at thecrop's location may be leached away by excessive moisture and soilsaturation. The fertilizer forecasting service 277 may also be usedretrospectively to evaluate fertilizer effectiveness loss due to soiland rain conditions that have recently occurred.

Another advisory service 270 as an output of the augmented crop growthmodel 100 is a pest prediction service 278. The presence of pests hasdiffering impacts depending upon the stage of a crop. Similarly, thevariability of the weather can give rise to potential crop diseaseconditions that also have differing impacts depending upon crop stage.The timeline identification of the various crop stages in theaccumulated growing degree day estimation 120 is a valuable tool fordeciding the best course for integrated pest management activities. Thisinformation, when incorporated in the pest prediction service 278,provides assessments of the conduciveness of weather conditions tovarious disease and pest pressures at the current time through the endof the growing season. The pest prediction information from thisadvisory service 270 helps in assessing the relative risk vs. rewardi.e., cost vs. benefit, of potential pesticide applications to preventcrop damage due to factors such as disease or insects.

Another advisory service 270 as an output of the augmented crop growthmodel 100 is a “harvest helper” reporting service 279. Theroutinely-updated accumulated growing degree day estimation 120 of thepresent invention provides an important metric for determining when acrop will reach maturity. However, it should be noted that the harvestof a crop involves factors such as appropriate weather conditions at thetime of maturity to permit a successful harvest. The harvest helperreporting service 279 estimates the timing of harvest operations, andweighing the relative costs of fuel-based forced-air drying vs. therisks associated with loss of standing crop to adverse weatherconditions. The harvest helper reporting service 279 also providesinformation on possible loss of field workability due to the formationof frost in the soils prior to post-harvest tillage. This reportingservice 279 may be further used to develop tools that include estimatingstanding crop dry-down rates, anticipated harvest dates and suitability,fuel consumption optimizers for forced-air drying, and indicators ofplant ‘toughness’ for anticipating harvest windows.

Another advisory service 270 as an output 150 of the augmented cropgrowth model 100 is a “ClearPathAlerts” service 280. The importance ofinformation provided to production agriculture in the present inventiondictates a necessity for an effective and direct method of conveyinginformation. The ClearPathAlerts service 280 may utilize a ‘push’technology for immediate and direct dissemination of informationprovided as output data 150 from the augmented crop growth model 100.The API module 260 that generates this service 280 may be configured toso that users may receive alerts of important combinations of weatherconditions, risk factors, and decision-support aides for the purposes ofmanaging and timing field operations. This is supported byuser-specified elements including alert parameters, location(s), leadtime(s), time(s) of day, and the particular device(s) to receive thealerts.

The systems and methods of the present invention may be implemented inmany different computing environments 280. For example, they may beimplemented in conjunction with a special purpose computer, a programmedmicroprocessor or microcontroller and peripheral integrated circuitelement(s), an ASIC or other integrated circuit, a digital signalprocessor, electronic or logic circuitry such as discrete elementcircuit, a programmable logic device or gate array such as a PLD, PLA,FPGA, PAL, and any comparable means. In general, any means ofimplementing the methodology illustrated herein can be used to implementthe various aspects of the present invention. Exemplary hardware thatcan be used for the present invention includes computers, handhelddevices, telephones (e.g., cellular, Internet enabled, digital, analog,hybrids, and others), and other such hardware. Some of these devicesinclude processors (e.g., a single or multiple microprocessors), memory,nonvolatile storage, input devices, and output devices. Furthermore,alternative software implementations including, but not limited to,distributed processing, parallel processing, or virtual machineprocessing can also be configured to perform the methods describedherein.

The systems and methods of the present invention may also be partiallyimplemented in software that can be stored on a storage medium, executedon programmed general-purpose computer with the cooperation of acontroller and memory, a special purpose computer, a microprocessor, orthe like. In these instances, the systems and methods of this inventioncan be implemented as a program embedded on personal computer such as anapplet, JAVA® or CGI script, as a resource residing on a server orcomputer workstation, as a routine embedded in a dedicated measurementsystem, system component, or the like. The system can also beimplemented by physically incorporating the system and/or method into asoftware and/or hardware system.

Additionally, the data processing functions disclosed herein may beperformed by one or more program instructions stored in or executed bysuch memory, and further may be performed by one or more modulesconfigured to carry out those program instructions. Modules are intendedto refer to any known or later developed hardware, software, firmware,artificial intelligence, fuzzy logic, expert system or combination ofhardware and software that is capable of performing the data processingfunctionality described herein.

It is to be understood that other embodiments will be utilized andstructural and functional changes will be made without departing fromthe scope of the present invention. The foregoing descriptions ofembodiments of the present invention have been presented for thepurposes of illustration and description. It is not intended to beexhaustive or to limit the invention to the precise forms disclosed.Accordingly, many modifications and variations are possible in light ofthe above teachings. It is therefore intended that the scope of theinvention be limited not by this detailed description.

The invention claimed is:
 1. A method comprising: inputting a pluralityof input data that includes crop-specific information, plantingspecifications, remotely-sensed imagery, and meteorological data thatincludes dynamical extended range weather forecast information, thedynamical extended range weather forecast information including aplurality of weather forecasts generated by independent sources, andcovering a time period extending from a current day to a maturity datefor a crop in a particular field; modeling the input data in a pluralityof data processing modules within a computing environment in which theplurality of data processing modules are executed in conjunction with,and performed on, at least one computer processor, the data processingmodules configured to profile crop development through to a maturitystage by modeling crop growth over the course of a crop growing season,by developing a specific crop growth model for analyzing past andpresent growth stages of the crop, to obtain at least one temporalestimate of crop growth for a remainder of the crop growing season, thespecific crop growth model developed by 1) applying a growing degree daymodel to historical and current weather information for a location ofthe particular field to generate a profile of past and present growthstages of the crop, 2) assessing the past and present growth stages ofthe crop on one or more dates by analyzing the remotely-sensed imageryof the crop captured on the one or more dates, 3) adjusting the profileof past and present growth stages based on assessments of the past andpresent growth stages on the one or more dates, and 4) generating aplurality of independent forecast profiles of further crop growth,starting from the current date and extending through to the time of cropmaturity, by applying the plurality of dynamical extended range weatherforecasts generated by independent sources in the growing degree daymodel; and applying the plurality of independent forecast profiles offurther crop growth to identify ranges of future dates on which the cropwill reach remaining crop growth stages in the crop growing season; andgenerating one or more of a crop growth action report and a crop summaryreport representing the at least one temporal estimate of crop growthfor the remainder of the crop growing season.
 2. The method of claim 1,wherein the crop-specific information includes a crop type and a varietyof crop type that determines a set of growth characteristics of the cropthat enables the data processing modules to profile crop development,the set of growth characteristics including a timing to growth of eachcrop growth stage, nutrient requirements per crop growth stage, pest anddisease susceptibility per crop growth stage, and one or moretemperature thresholds for crop growth conditions.
 3. The method ofclaim 1, wherein the meteorological data further includes observedweather data for a geographical area including the crop's location, andcurrent field-level weather data for the geographical area including thecrop's location, the observed weather data and the current field-levelweather data being repetitively applied during a time period where thecrop maturity date has not yet been reached.
 4. The method of claim 3,further comprising generating an accumulated growing degree day estimateon a daily basis until a total accumulated growing degree day estimateexceeds the time period extending through to the crop maturity date. 5.The method of claim 1, further comprising analyzing the remotely-sensedimagery using a normalized difference vegetative index to evaluate atleast one of plant health, biomass, and nutrient content.
 6. The methodof claim 1, further comprising generating a notification to a user whena validation of an estimate of crop growth stages produces a change inthe map of the crop's location.
 7. The method of claim 1, furthercomprising providing a map of the crop's location to one or moreapplication protocol interface modules each configured to generate anadvisory service for agriculture management applications.
 8. The methodof claim 1, further comprising providing a map of the crop's location toone or more application protocol interface modules each configured toprovide a crop alert to a user.
 9. The method of claim 1, wherein theremotely-sensed imagery of the crop's location is captured by one ormore unmanned aerial systems.
 10. The method of claim 1, wherein theremotely-sensed imagery of the crop's location is captured by one ormore satellite systems.